Lego and Data: Closer than you think

In this article, Chris Pham, a Senior Business Intelligence Analyst, Talent Operations at LinkedIn, demonstrates how playing with Lego and working with data are closer than you think.

More often than not, there’s a slightly surprised look on people’s faces when they find out that I love playing with Lego. One reason may be because Lego is associated with creativity and design, two things that don’t immediately come to mind when you hear about my role at LinkedIn (business intelligence for HR)*. In the spirit of LinkedIn’s last InDay theme, Creativity, here are 5 experiences I’ve had that show how playing with Lego and working with data are closer than you think.

1. The best creations rely on the simplest pieces

The original Lego patent was for a simple 2 x 4 brick. Today, there are 6,800 distinct Lego bricks available (and that’s down from 12,000 a few years ago!). In my opinion, the true Master Builders are those who can use simple pieces in unconventional ways instead of relying on the highly specialized ones. There are 24 different ways to combine two 2 x 4 bricks; with 6 of those bricks, the number of combinations jumps to 915,103,765.

Let’s be real here – the HR function has never had a reputation of being data driven. When I think about what it takes to help HR get to the next level, I think back to the original 2 x 4 Lego brick. Yes, we can jump right into predictive analytics when we have the right data – in other words, those highly specialized bricks – but the journey begins with something as simple as figuring out how many recruiters are needed to meet next year’s hiring targets. It’s not the sexiest thing to work on, but sometimes it’s the basic things that provide the most business impact.

Most of the time, I didn’t have the right pieces to build something exactly the way I wanted it, so I had to look for substitutes or rethink how my Lego model was going to be made. I learned to use what I had in a different way. Similarly, if your user-generated data is incomplete because there’s no process to guarantee data integrity, there’s still insight to be drawn from even the most limited data as long as you’re creative about where you look.

2. Both require an iterative mindset

The beauty of Lego is that you’re not restricted to what’s on the box. Rebuilding something and improving on it each time requires an iterative mindset, and when it comes to working with data, there are plenty of opportunities to iterate. I usually build the box model once and then try to improve on it by making some adjustments. Then I take it apart and make more tweaks. Sometimes my imagination leads me to something completely different.

Today, when I reach a “good enough” solution, whether it’s a dashboard or a python script, I still find time to destroy it, rebuild it, and continue to improve. It may get the job done, but chances are I can rebuild it into something more efficient and scalable. I get the same joy out of building the same robot with fewer pieces, as I do writing a query with fewer lines.

3. Don’t cut corners when it comes to documentation

The saddest part about playing with Lego is taking apart my beloved creations because I need to use the pieces for something else. There are many things I built as a kid that I really wish I could recreate today, but unfortunately they will never exist again because I didn’t take enough pictures or record which pieces were needed.

Imagine yourself in a scenario where you revisit an analysis you had done a year ago, and you struggle to achieve the same output or reach the same conclusions you did the first time around. If you want to be able to recreate that beloved creation of yours, make an instruction booklet. I learned that the hard way many, many times. Can you imagine opening a new Lego set and finding out you don’t have the instructions?

4. Search and exploration are the most fun parts of the process

When I was a child, I liked to mix all of my Lego bricks together in a giant tub because a lot of the fun in building something was searching through a sea of bricks and trying out new connections that I didn’t think of before.

Anybody who works with data knows that as much as 80% of the job is cleaning your data and performing exploratory analyses. Personally, that’s what I love about working with data – this is where I let my creativity and imagination run free. Jumping right into a large dataset and testing out different visualizations and correlations, in search of new connections/patterns takes me back to a childhood spent digging through a pile of Lego – damn it, I swear I saw that piece at least 20 times when I didn’t need it!

5. Designing for play vs. designing for aesthetic

The name Lego is derived from the Danish phrase leg godt, which means “play well”. Before building something with Lego, I decide whether it’s something I want to display, or something I want to move around. With display pieces, I could get away with weaker connections, but if it was something I wanted to play with, I knew I had to make it extra stable. After all, it would be very disappointing if my spaceship’s wings fell off while I was swooshing it around the room.

When making a dashboard, data tool, or even a report, I always start by asking myself whether this is something people will actually use (i.e. play with), or if it’s something they want to see once and never again. Then I design and build accordingly. When I need to drive adoption, I focus on every detail: how the filters should work; what should appear when a user hovers over an object; speed; and design, formatting, and coloring of my data visualizations.

I strive to make things that last, no matter how many times I swoosh them around the room.

Chris Pham is a Senior Business Intelligence Analyst, Talent Operations at LinkedIn, where he builds models, dashboards, and visualizations with data from the company's large member base and applicant tracking system to guide decisions around talent.
In his spare time, Chris enjoys learning new skills, building things, and drinking single origin coffee (right now, it's Kenyan).